SeptiSearch Tutorial

Expore the Collection by Study

The first tab allows you to easily browse and search the data we’ve curated. The first field in the sidebar allows you to enter search terms to filter the entries based on the title of study/article. For example, you could enter “COVID” to display all studies we’ve gathered with a focus on COVID-19 infections in the context of sepsis.

The second field facilitates searching our data for specific molecules, and will display any studies whose signature includes the molecules you enter. Note that this searching is case-sensitive, but does allow for partial matching of names (e.g. entering “S100” will return entries including S100A8, S100A9, etc.).

The final field allows you to filter the table for a specific omics type, namely Transcriptomics or Metabolomics.

If, after entering a number of searches or filter criteria, you would like to return to the original table, simply click the “Restore defaults” button at the bottom of the sidebar, which will reset all inputs. In some cases it may also help to simply refresh the page from your browser.

Select a study from the table to display all associated molecules.


The table on the right will display all matching studies based on the specified filters - initially it simply lists all studies curated in SeptiSearch. Clicking on a row will bring up a second table listing all entries (i.e. molecules) for that particular study. Multiple rows can be selected to list molecules from a number of studies simultaneously. A small search box just above this second table (on the right-hand side) will allow to to search all columns/rows of the study-specific table. For example, you can click on the entry for “A single-cell atlas of the peripheral immune response…” in the top table, then use the lower search box to look for S100 molecules within that signature, returning 10 entries from the total 231 molecules. When you have selected on one or more rows from the top table, you can use the provided button to download the lower table as a plain text, tab-delimited table, which can be easily viewed in, for example, Excel.

Visualize the Top-Occurring Molecules

The second tab provides a more visual way of search the curated signatures, with a greater focus on the molecules collected in SeptiSearch.

The plot shows the most common molecules in the dataset for the given time point. The sidebar on the left provides a number of ways to filter the data, which will update the plot to show the top-occurring molecule which matches the specified criteria. Note the possible options for each input will update based on other criteria you’ve selected. Again, a “Restore defaults” button at the bottom of the sidebar will reset all inputs and return the plot to its original state.

Hover over a column in the plot to see the details for that molecule.


At any time, you can click one of the bars (molecules) in the plot to show all entries for that molecule in SeptiSearch. Keep in mind that this reactivity is also sensitive to the time point, as well as molecule. This table can also be downloaded using the button at the bottom of the sidebar.

Perform GSVA with Sepsis Signatures

One of the great features of SeptiSearch is the ability to apply our curated sepsis signatures to your own data using Gene Set Variation Analysis (GSVA). This method identifies sets of genes (in our case, the sepsis signatures) within expression data produced by technologies such as RNA-Seq or microarrays. Each sample within your dataset is assigned an enrichment score for each gene set, which can be positive or negative depending on the expression of the genes in a given sample. For example, you may be able to identify that certain sepsis signatures are expressed more highly in certain treatment groups than others.

To help demonstrate the usefulness of GSVA, we provide an example dataset for you to try out, all with a simple button press! By clicking “Load example data”, a subset of the microarray data from GSE65682 (both the expression data and sample labels, a.k.a. metadata) is loaded by the app and made ready for testing. Then simply click the “Submit expression data for GSVA” button at the bottom of the sidebar to see how the results look.

Successfully loaded user data for GSVA.


If you’d like to submit your own data for GSVA, the expression component needs to meet certain criteria:

  • Your data must in a plain text, comma-delimited (CSV) file
  • The first column must contain Ensembl gene IDs
  • The remaining columns should contain samples, with unique names
  • Values within the matrix should be transformed/normalized, as is appropriate for your data/technology
    • We provide a link to information on the Variance Stabilized Transformation (VST) which is often suitable for RNA-Seq data, but other methods may also be used

While you can submit only the expression data for GSVA, it’s recommended to also include some metadata (e.g. sample groups) to make the final heatmap more informative. There are a few requirements for submitted metadata:

  • Your metadata must be in a plain text, comma-delimited (CSV) file
  • The first column should contain sample names, which must match the sample names from your expression data
    • Any missing, extra, or non-matching sample names will generate an error
  • All remaining columns will be considered variables/traits (e.g. disease status, condition, etc.)

Once you’ve uploaded either the expression data alone or expression and metadata, click the “Submit expression data for GSVA” to run the analysis and view the results, which include a summary table and heatmap visualization. Explanations of all the columns can be viewed by hovering over the column names with your cursor.

Results from SeptiSearch’s GSVA implementation.


The full results table can be downloaded with the button at the bottom of the sidebar, and the image can be saved by right-clicking on it and selecting “Save Image…” from the context menu.

Perform Pathway Enrichment

As an extra feature of SeptiSearch, we’ve added the ability to upload a list of your own genes (e.g. those identified as differentially expressed in an RNA-Seq experiment) and test them for enriched Reactome pathways, Hallmark gene sets and GO terms. This functionality is designed to complement the use of GSVA, which tests your data for the dysregulation of our curated sepsis signatures.

Inputting data for pathway enrichment.


To get started, you’ll need a list of genes as a single column, with one gene per line; you can copy them from an Excel spreadsheet or plain text file. The genes IDs can be Ensembl, Entrez, or HGNC gene symbols. Any of these three inputs is fine, as long as they’re consistent for the whole list (i.e. you cannot submit a mixture of gene ID types). Once you’ve pasted in your genes, the “1. Perform gene ID mapping” button will be enabled; use it to map your input genes to the two remaining types (i.e. if you provide Ensembl genes, they will be mapped to Entrez IDs and HGNC symbols). This step is necessary because the two tools being used (ReactomePA and enrichR) require different input types. Once this step has been successfully completed, you can then hit the “2. Submit genes for pathway enrichment” button to run the tests. The results will be displayed in two tables; the upper containing Reactome pathways identified by ReactomePA, and the lower containing GO terms and MSigDB Hallmark gene sets found by enrichR. Hover your cursor over the column names to see a description, if anything is unclear. Both tables can be downloaded using the relevant buttons at the bottom of the sidebar.

Top table of enrichment results, from ReactomePA.

Lower table of enrichment results for enrichR.


Just like the previous “GSVA” tab, we have provided an example list of genes, based on one of the sepsis signatures (Xiong et. al.) which can be used as a demonstration of this page’s functionality. To get started, simply click the “Load example data” button; a prompt will appear with details about the example set (namely the number of genes and type of identifier). You can dismiss the prompt, and select the “1. Perform gene ID mapping” button to perform the gene mapping step. Once this has completed, simply click the “2. Submit genes for pathway enrichment” button to test the example input genes and view the results.